Associate Professor Tao Wang
Deputy Director of the Institute of Advanced Technology, Sun Yat-sen University
Associate Professor of the School of Sun Yat-sen University
Brief Introduction: He is a talent introduced by Sun Yat-sen University‘s Hundred Talents Program, and is mainly engaged in the research of precision guidance, multi-domain unmanned system coordination, cognitive intelligence technology, and game confrontation decision-making application technology. He has worked for the First Academy of China Aerospace Science and Technology Corporation for a long time, and has undertaken more than 20 pre-research projects and model projects of various national and military types, independently responsible for more than 20 million scientific research funds.
Speech Title: Research on key technologies and demonstration experiments of self-organizing collaborative cognitive confrontation decision-making for multi-domain unmanned systems
Speech Abstract: In view of the practical application problems such as credibility, explainability, reliability and scalability faced by the collaborative cooperation and confrontation decision-making of multiple unmanned systems in complex dynamic environments, it is proposed to build a general framework for collaborative cognitive confrontation decision-making of multiple unmanned systems based on the human-like cognitive architecture. By designing cross-platform application simulation modeling experiments, the framework is explored and verified to achieve real-time, efficient, reliable and traceable collaborative decision-making capabilities in the multi-domain unmanned system operation environment, providing a self-evolving unified standard decision-making model framework solution for the future unmanned system cluster collaborative application. A simulation and scaled model demonstration and verification system for key technologies of self-organizing collaboration of multi-domain unmanned systems is established, and technical function and performance verification tests are carried out to verify the feasibility and availability of algorithm models and technical performance.
